TY  - BOOK
AU  - Vandeput,Nicolas
AU  - Makridakis,Spyros
AU  - Ndiaye,Alassane B.
TI  - Data Science for Supply Chain Forecasting
SN  - 9783110671100
AV  - QA76.9.D343 V36 2021
PY  - 2021///]
CY  - Berlin, Boston : 
PB  - De Gruyter, 
KW  - Business forecasting
KW  - Data processing
KW  - Data mining
KW  - Statistical methods
KW  - Python (Computer program language)
KW  - BUSINESS & ECONOMICS / Total Quality Management
KW  - bisacsh
KW  - Data science
KW  - De Gruyter
KW  - Forecasting
KW  - Machine learning
KW  - Nicolas Vandeput
KW  - Overfit
KW  - Python
KW  - SKU Science
KW  - SupChains
KW  - Supply chain forecasting
KW  - Supply chain
KW  - Underfit
KW  - demand forecasting
KW  - inventory optimisation
KW  - inventory optimization
KW  - multi-echelon optimisation
KW  - multi-echelon optimization
KW  - supply chain data science
KW  - supply chain management
N1  - Frontmatter --; Acknowledgments --; About the Author --; Foreword – Second Edition --; Foreword – First Edition --; Contents --; Introduction --; Part I: Statistical Forecasting --; 1 Moving Average --; 2 Forecast KPI --; 3 Exponential Smoothing --; 4 Underfitting --; 5 Double Exponential Smoothing --; 6 Model Optimization --; 7 Double Smoothing with Damped Trend --; 8 Overfitting --; 9 Triple Exponential Smoothing --; 10 Outliers --; 11 Triple Additive Exponential Smoothing --; Part II: Machine Learning --; 12 Machine Learning --; 13 Tree --; 14 Parameter Optimization --; 15 Forest --; 16 Feature Importance --; 17 Extremely Randomized Trees --; 18 Feature Optimization #1 --; 19 Adaptive Boosting --; 20 Demand Drivers and Leading Indicators --; 21 Extreme Gradient Boosting --; 22 Categorical Features --; 23 Clustering --; 24 Feature Optimization #2 --; 25 Neural Networks --; Part III: Data-Driven Forecasting Process Management --; 26 Judgmental Forecasts --; 27 Forecast Value Added --; Now It’s Your Turn! --; A Python --; Bibliography --; Glossary --; Index; restricted access; Issued also in print
N2  - Using data science in order to solve a problem requires a scientific mindset more than coding skills. Data Science for Supply Chain Forecasting, Second Edition contends that a true scientific method which includes experimentation, observation, and constant questioning must be applied to supply chains to achieve excellence in demand forecasting. This second edition adds more than 45 percent extra content with four new chapters including an introduction to neural networks and the forecast value added framework. Part I focuses on statistical "traditional" models, Part II, on machine learning, and the all-new Part III discusses demand forecasting process management. The various chapters focus on both forecast models and new concepts such as metrics, underfitting, overfitting, outliers, feature optimization, and external demand drivers. The book is replete with do-it-yourself sections with implementations provided in Python (and Excel for the statistical models) to show the readers how to apply these models themselves. This hands-on book, covering the entire range of forecasting—from the basics all the way to leading-edge models—will benefit supply chain practitioners, forecasters, and analysts looking to go the extra mile with demand forecasting
UR  - https://doi.org/10.1515/9783110671124
UR  - https://www.degruyter.com/isbn/9783110671124
UR  - https://www.degruyter.com/document/cover/isbn/9783110671124/original
ER  -